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[Whitepaper] Robots in Recruiting - The Implications of AI on Talent Acquisition

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Robots in Recruiting
The Implications of AI on Talent Acquisition

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About the Author
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Allan Schweyer
Allan Schweyer is the Founder of TMLU, Inc. in which he develops
and delivers curriculum and conducts human capital management
related research for a wide variety of clients including multiple
government agencies, the Incentive Research Foundation, Lockheed
Martin, and many others.
Allan is a recognized subject matter expert in human capital
management. Prior roles include Staﬃng Subject Matter Expert at
HR.com, President and Executive Director of the Human Capital Institute
(HCI) and partner at the Center for Human Capital Innovation.
Allan is the author of the books, Talent Management Systems (Wiley &
Sons, 2004) and Talent Management Technologies (HCI Press, 2009) and
co-author of the Enterprise Engagement Textbook (2014). Over the past
twenty years, he has published extensive articles and white papers in
dozens of popular media and industry-speciﬁc publications worldwide,
including Inc. and The Economist Magazines. Allan has been recognized
as among the “100 Most Inﬂuential People in HR and Talent
Management.”
Robots in Recruiting

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Introduction
Earlier this year, IBM added second opinion services to its employees’ health plans. Most of the
advice doesn’t come from a doctor, however, it is provided by Watson, IBM’s artiﬁcial intelligence
(AI) system. Watson boasts one of the world’s most advanced neural networks, enabling it to
detect things like tumors long before physicians can.1
Woodside Energy uses AI to manage three
decades of organizational implicit knowledge so that technicians can get precise information just
by asking. Amazon recently opened the ﬁrst of many planned “Go” stores in which there are no
employees and no check-out lines. AI tracks what you take and puts it in your online shopping
cart.2
GlaxoSmithKline and Toyota use AI to develop ads that tap real-time data and use natural
processing language to interact with customers and answer their questions.3
Broadly deﬁned, the term artiﬁcial intelligence (AI) is applied to machines and algorithms that
mimic the cognitive functions of human beings, including sight, touch, speech recognition and
problem-solving. But for the time being, AI augments and/or automates only speciﬁc tasks; for
example, medical diagnoses or playing Jeopardy. It does not and may never possess a “general
intelligence” similar to the human brain.4
AI machines are constantly improving though. This
ability to learn creates unprecedented, even unlimited opportunities, as well as challenges,
because the machines advance at superhuman speed and scale.5
Though they are often invisible, machines, robots and AI are now ubiquitous. They work behind
the scenes, in warehouses and factories, and in computers that aid everyone from radiologists to
data scientists. They assist “knowledge workers” in every almost every oﬃce, hospital and
construction site. Already, AI-equipped machines operate entire restaurants, they drive cars and
create works of art, all by themselves.6
At some point in the future, their capabilities
—intelligence if you prefer—may surpass humans’ by a factor of thousands or even millions.
The implications for the recruiting profession are immense, just as they are for professionals in
almost every other discipline. While the impact on jobs for recruiters—on a large scale—may
occur only gradually over the next decade or so, another major implication of AI in recruiting is
already upon us. Namely, the myriad ways in which AI is changing the work of recruiters
today—particularly in sourcing and initial screening.
Accelerating artiﬁcial intelligence (AI) capabilities will enable automation of
some tasks that have long required human labor. These transformations will
open up new opportunities for individuals, the economy, and society, but they
have the potential to disrupt the current livelihoods of millions of Americans.
—Executive Oﬃce of the President, December, 2016
Robots in Recruiting

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Predictive statistical analysis, harnessed to big data, appears poised to alter
the way millions of people are hired and assessed.
—Don Peck, The Atlantic
Part One
AI in Recruitment
Today
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Job Advertising
By 2017, it is expected that the majority of ad buying and placement in the U.S. will be
automated. Machines will bid out ad space and other machines will buy it. Already, more than
two-thirds of digital advertising is automated, rising to almost three quarters in 2017. Since
almost all job advertisements today are digital, the rapid and full-on automation of recruitment
advertising—programmatic recruitment advertising (PRA) is inevitable.
Yet, PRA is only in its infancy today. According to Chris Forman, founder and CEO of PRA provider
Appcast, it cannot really be described as AI. Though sophisticated, the software and algorithms
used in PRA models today are hard-coded—they don’t learn on their own. Nevertheless, they
optimize the placement of ads, increasing the odds that well-suited candidates will apply to
positions. Ads are delivered based on what the algorithm knows about each person’s profession,
skills, interests, where they “hang out” on the web—even whether or not they are active in their
job search.12
Amsterdam-based Recruitz.io employs technology like Appcast’s to deliver ads to users of
non-job platforms—social media and Google users, for example. The algorithms use rich
user-proﬁle information provided by social media sites and combine it with behavioral data to
deliver highly targeted job ads. The technology learns, in a sense, by performing hundreds, even
thousands of small experiments known as A|B tests in real time, to determine what is more likely
to cause a person to click on an ad and optimize cost and quality.
The algorithms determine what sort of images appeal to each job seeker, what time of day they
are most likely to be receptive and, of course, what job titles and content within the ad are more
likely to get and keep their attention. Recruitz.io also employs a chatbot tool named Adam who
interacts with recruiters. As a user-friendly interface to Recruitz.io’s complex algorithms, Adam
simply asks recruiters to describe the candidates they’re looking for, and the AI does the rest.13
Programmatic recruitment advertising—though only emerging as a technology today—targets a
component of recruiting that is ripe for automation. Even large teams of recruiters working
full-time cannot eﬀectively analyze all of the places their ads might appear, then determine the
best places for each ad, the right amount to pay for those ads, and precisely when to place them,
remove them, or adjust them in the midst of a campaign. Programmatic technology performs
these calculations in microseconds, millions of times per day, saving signiﬁcant time and money
for recruiters, while also improving outcomes—the very recipe for automation.
Robots in Recruiting

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Automated Matching
More than half of recruiters report that sifting through potential candidates is the hardest part of
their work.14
There are hundreds of millions of candidate proﬁles and resumes across the
internet. No recruiter, not even a Boolean black belt, can cope with those quantities.15
AI
surpasses Boolean search in its ability to ﬁnd matches that don’t contain speciﬁc keywords. It
does this based on correlations and through constant learning as it determines what skills and
other attributes are similar, and/or often appear in combination.
Automated candidate search has made recruiting tools like LinkedIn powerful platforms for
recruiters. LinkedIn’s Talent Solutions group already accounts for the majority of its revenue, and
recruiters form its biggest group of paying members. LinkedIn’s recent AI oﬀerings allow
recruiters to match top performer proﬁles to the database to ﬁnd others like them.16
One can
easily imagine a future in which LinkedIn ﬁnds matches, converses with prospects, answers their
questions, and conﬁrms their interest—then sends hiring managers three or four top candidates
for interviews.
Many other intelligent tools are beginning to ﬁlter whole populations for recruiters, as well.
Joberate, for example, is a talent search and analytics technology provider that has devised what
it calls the “J-Score” to assign those who use social media to look for a new career opportunity, a
job seeking score. According to CEO Michael Beygelman, a higher J-Score, means a person is
more active in their search. This helps recruiters ﬁnd and target people who are most open to
their message.
The J Score is generated in the background based on what employees and job seekers are
actually doing—for example, changes they make to their online professional proﬁles,
professional connections, career-related content they follow or like, etc. Joberate boosts
application conversion to interview rates by as much as 90 percent by allowing recruiters to
target their approach to candidates based on their J Score. Recruiters zero-in on potential
candidates with high J-Scores, while managers might monitor aggregate team J-Score information
to gain a sense of engagement and the need for retention interventions.17
The J-Score is becoming a ubiquitous metric by which job seekers signal their
willingness to entertain new opportunities without explicitly having diﬃcult or
awkward conversations with their boss or headhunters.
—Michael Beygelman, Joberate
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Candidate Experience
Just as recruiters dislike sifting through resumes and proﬁles, job seekers are exhausted by
endless job postings. Today’s programmatic recruitment advertising tools help candidates in
much the same way they aid recruiters—through increasingly tighter targeting. According to Lars
Wetemans of Recruitz.io, PRA helps candidates pinpoint ads that match their skills, interests, level
of experience, location, etc. He also points out that programmatic technology helps people see
jobs they would not otherwise have found, much like it helps recruiters ﬁnd candidates they
might not otherwise have discovered.18
Dave Berthiaume of Goodwill Easter Seals MN uses PRA provider Appcast, and though he hasn’t
heard an applicant state explicitly that they appreciate targeted job ads (candidates may not
know, after all, that algorithms are at work behind the scenes), he believes that many are pleased
and surprised to receive ads that are so well suited to them. Berthiaume is convinced that better
targeted ads result in more and better applications for Goodwill.19
Though wading through countless job ads is irritating, even greater aggravation occurs after
candidates have applied to positions. More than half of candidates complain that waiting to hear
back from a potential employer is the most exasperating part of looking for work.20
Most
recruiters genuinely want to manage the candidate experience but are often overwhelmed with
other work. Again, AI and automated tools can help close this gap.
“Chatbots,” for example, promise to improve or even eliminate the “black hole” candidates face,
by managing the experience and engagement of all applicants. At FirstJob, virtual recruiting
assistant Mya augments the work of recruiters, acting as a bridge between them and the
candidate. According to CEO, Eyal Grayevsky, Mya engages in intelligent conversations with each
and every candidate. She reviews their application with them, answers questions about the hiring
process, and—should she assess the candidate as qualiﬁed and interested—schedules their ﬁrst
interview. Mya rejects candidates gently, suggesting other job openings they may be qualiﬁed for
and/or inviting them to register in the talent pool.21
Mya also learns. She becomes a better conversationalist with each candidate she encounters. As
dozens and then hundreds of organizations deploy her—and as a few conversations per hour
turn into hundreds or thousands—the data from every conversation she engages in improves
her subsequent interactions.
Moreover, Mya and other chatbots are always available—candidates can chat them any time, day
or night, to ask about the status of their application and where they stand. If a candidate doesn’t
initiate conversation, the tool can send them email updates and encourage them to check back.22
Robots in Recruiting

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Though Firstjob has only recently begun testing Mya with clients, it appears that she is oﬀ to a
promising start. Mya persuades over 90 percent of applicants to go through initial vetting and
matching with her, and she convinces more than 80 percent of candidates who drop oﬀ in the
process to re-engage. Both numbers represent signiﬁcant improvement on industry benchmarks
according to Grayevsky, who describes Mya as an optimized, fully automated conversion funnel.22
ThisWay Global takes a diﬀerent approach to reinventing the broken system of job search and
recruiting through machine learning/AI enabled platforms. ThisWay launched from ideaSpace at
the University of Cambridge in England, following two years of R&D. Its goal is to provide
advanced job matching that will address the hiring problems that businesses and individuals
face. A key component of the solution is to create a complete ontology and taxonomy for the
language of recruiting and job search, and to build machine learning on top of it in order to
prevent AI from perpetuating the same biases that cause humans to make poor hiring and job
choices.
ThisWay improves job matching by building on behavioral learning from users’ interaction with
the platform, combined with what they do at work and what they enjoy most in their personal
lives.23
It also uses game-like interactions with job seekers to generate more accurate
assessments of their attributes and traits. A person who completes their proﬁle (called a
“passport”) might see questions related to risk tolerance, such as: “If a spaceship landed in your
front yard, what would you do?” According to CEO Angela Hood, when provided with these sorts
of scenarios, most people know exactly what they would do, there is no middle ground. Thus,
responses are more natural and accurate than the more direct and typical: “please rank your
capacity for risk” approach.
Combined with knowledge of the candidate’s leisure activities—for example, rock climbing versus
mountain biking—and work behavior, ThisWay builds a more complete assessment of a
candidate’s risk tolerance and other essential human attributes that improve matching. This
information matters depending on the position you’re looking to ﬁll—a salesperson versus an
accountant, for example. In all, ThisWay’s AI and predictive algorithms currently consider about
36,000 data points, risk aversion being just one. Ultimately, this beneﬁts job seekers by allowing
them to see a personalized universe of strongly matched positions without having to search.
Recruiters beneﬁt from having better matched candidates introduced to both their business and
the job opportunity.24
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Initial Screening
Mya, the chatbot introduced above, performs the dual role of managing candidate relationships
(at least in the early stages) and—if desired by the client—she performs a ﬁrst level screen. In
high volume recruiting in particular, AI can have a tremendous impact. One Mya user hires about
20,000 warehouse workers for the holiday season and processes over 140,000 applicants—all in
three months. Mya automates the initial screen to decide whether each applicant should be
forwarded or rejected, saving recruiters hours of manual candidate screening. This isn’t dissimilar
to the pre-screening questions many applicant tracking systems have featured for years, but it
goes further because Mya conducts a dynamic interview with every candidate. She asks a wide
range of contextual questions, generated in real time, in response to the ongoing conversation.
This results in enhanced candidate engagement, deeper screening and greater qualiﬁcation
accuracy. As above, Mya then schedules qualiﬁed candidates into interviews.
Passive big data techniques are also worth considering. Transcom, a global operator of customer
service call centers, for example, has been working with the assessment technology ﬁrm, Evolv
(now owned by Cornerstone OnDemand) for several years. Evolv analyzes email, keystrokes and
other “ambient” data generated by employees in the course of their work. Evolv’s eﬀorts to
reduce attrition among Transcom’s call center employees has resulted in 30 percent reductions
so far.25
This improvement has encouraged Transcom to expand its work with Evolv to ﬁnd traits that
predict which job candidates for other positions might stay the longest if hired. One eﬀort, a pilot
project using Evolv’s data analysis technology, was conducted in 2012 to assess candidates for
honesty. Transcom knew from prior analysis that honest candidates typically stay in their jobs
almost a third longer than those who aren’t. In a call center, this is signiﬁcant. The Evolv system
has helped Transcom uncover traits like honesty in very simple and elegant ways.26
Robots in Recruiting

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The current system is absolutely appalling for both sides, but there is so much
money made in this chaos that it’s self-perpetuating.
—Angela Hood, CEO ThisWay Global
Part Two
The Implications
of AI in Sourcing
& Screening
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Overcoming Bias and Fatigue
Aside from the enhanced speed and accuracy, lower costs and better candidate management
described in Part One, AI has the potential to reduce or eliminate discrimination from the
sourcing and screening process. Human bias and error often cause high quality applicants to be
missed or passed over before the interview stage. AI assesses candidates purely on their merits
—provided, of course, that bias isn’t inadvertently built into the system.27
Chris Forman of Appcast agrees that AI can address the issue of human bias but also overcome
problems related to human fatigue. Forman refers to research that consistently shows people
who apply to positions earlier in the process are more likely to be hired, simply because
recruiters grow exhausted as they wade through applications. “AI isn’t lazy. AI is self-taught and
never forgets anything. You can ﬁll it with the CVs of all of your high-performers and
high-potentials then boil the ocean to ﬁnd commonalities in applicants.” 28
Still, according to Forman, the idea that you can always predict who will do well—at least in their
ﬁrst year—is ﬂawed because so much depends on the manager and other variables. Combined
with experienced recruiters and managers however, AI can tell you that “Joe does well working
with female leaders, whereas Bob works better with ﬁnancial people. In these ways, AI can oﬀer
exponential improvements in the existing hiring process.” 29
AI in Late-Stage Screening and Selection
If AI’s performance bests recruiters where sourcing and initial screening is concerned, can the
same be said for selection? Surprisingly, a 2015 Harvard Business School paper suggests it can. In
a paper entitled ‘Discretion in Hiring,’ the authors go as far as to argue that managers should be
prevented from making hiring decisions in low-skilled positions. The researchers’ experiments
pitted data and algorithms against experienced managers and found that the algorithms do a
better job of choosing qualiﬁed applicants.30
If Henry Ford had queried algorithms for what his customers wanted, they would
have replied ‘a faster horse.’ In a world of big data, it is our most human traits
that will need to be fostered.
—V. Mayer-Schonberger & K. Cukier, Big Data
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In a separate, 2016 study published in the American Economic Review, researchers conclude that
for middle and higher skilled positions, machine learning and AI can enhance organizations’
ability to make better hires. Importantly, though, they did not conclude that the decision should
be left entirely up to the algorithms, only that the AI can help humans make better decisions. The
researchers determined that in order for the algorithms to perform well, they need experienced
recruiters to determine what they’re looking for.31
Research and experience to date demonstrates that AI and machine learning can relieve
recruiters of the entire task of sourcing (including ad buying and placement) and even initial
screening. For the adventurous, it might be deployed further to make ﬁnal selection decisions for
high volume, low-skilled positions. In ﬁlling middle and highly skilled positions, however, the
research suggests secondary screening, interviewing and the hiring decision should still be
performed and made by humans, perhaps assisted by AI.32
According to Michael Beygelman of Joberate, machine learning and AI constantly improve
sourcing and screening for certain types of positions. AI, however, can’t yet outperform a
recruiter or hiring manager when it comes to the question of “whether it is better to hire Bill or
Surita for certain professional roles.” Beygelman warns that conﬁrmation bias is often built into
any system that tries to predict which person to hire. “It tries to get to a conclusion that isn’t
always there. The whole prediction game is still too nascent to put into production for all
corporate use cases. For example, certain incarnations of AI-based predictions about a person’s
ﬂight risk have already led to law suits. AI, in fact, sometimes creates one of the problems it’s
trying to solve—adverse selection.” 33
Due to these issues, Beygelman believes AI and Machine learning—beyond sourcing and initial
screening—won’t fully catch hold for years and perhaps even a decade or more to come, except
among a few early adopters.
We like to work with recruiters by taking the ﬁrst 70 percent of the hiring process
—sourcing and screening, the part that is really fatiguing—and automate it.
The last 30 percent—the interviewing and relationship-building—is the part
the recruiters want to do and are better at than AI. AI and machine learning won’t
replace good recruiters who do that 30 percent.
—Angela Hood, CEO, ThisWay Global
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The Future of AI in Sourcing & Screening
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Eyal Grayevsky of FirstJob agrees. He points out that due to the wide range of nuances involved in
hiring—including politics and the views and needs of stakeholders—humans must make the
hiring decisions because AI can’t yet understand and factor for the non-binary variables in many
selection processes.34
Lars Wetemans concurs as well, aﬃrming that good recruiters are needed
in the process. Whereas advertising across multiple media channels, and optimizing ads using
mass A|B tests, simply cannot be done by hand, he says, “interviewing and selection is more
eﬀectively done by a recruiter.” 35
Again, the state of the art in broadly-deﬁned AI and machine learning today suggests that
organizations should improve, accelerate and reduce the costs of the recruitment process by
automating and using AI in everything from recruitment advertising and sourcing to initial
screening. In interviewing and selection, however, AI should be leveraged to inform better
decisions, not make them.
Given the trajectory of past technologies, it seems likely that challenges such as processing
limitations, data privacy, bias and other social issues surrounding AI will be resolved and it will
continue expanding.36
In the recruiting ﬁeld, great progress in the application of AI and machine
learning over the next few years would seem to be a safe prediction.
Chris Forman of Appcast predicts the rapid emergence of job ads that aren’t just targeted and
optimized by media and time of day but also timed to when people encounter life events that
might change their receptiveness to the right ad. Forman notes that it’s already possible to create
ads with embedded chatbots, which hold conversations with those who encounter them. As
described above with Mya, such ads used in recruiting will advise candidates on the match,
answer questions, help with applications, and gently guide unﬁt candidates to better
opportunities.37
Of course, no discussion of the future of AI in recruiting can avoid the question of what it might
do to overall employment. One thing is certain: AI and machine learning in recruiting will
improve. Remember that smart and inquisitive software mirrors human evolution in the sense
that genetic programming allows AI to improve itself through a Darwinian method of survival of
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AI recruiting software already operates faster than humans, and it exploits advantages that arise
and disappear in fractions of seconds. Eventually, it seems inevitable that it will understand the
entire process—from sourcing to selection—well enough to displace all human sourcing,
recruiting and hiring activity as we know it today.
In the short-term, recruitment automation appears likely to eliminate more jobs in the ﬁeld of
recruiting than it will create. Much of the intermediary role the recruiter plays between candidate
and hiring manager, for example, may be rendered redundant by the ease of candidate and
hiring manager self-service. Nonetheless, like the lawyer who used to spend enormous time
researching jurisprudence but now performs more sophisticated client advisory work, innovative
and creative recruiters will almost certainly have a place in the selection and hiring process for at
least a decade or more to come.
Most agree that for today at least, AI can only improve decisions in the late stages of the hiring
cycle, not make better decisions all on its own.
Conclusions
AI and machine learning, broadly deﬁned, have made signiﬁcant in-roads into the recruiting and
hiring process in just the past ﬁve years or so. Already, organizations put themselves at a deep
disadvantage in ﬁnding and hiring talent when they ignore or don’t understand the advantages of
automation and AI.
AI more than replaces the repetitive and time consuming work of sourcing, screening and early
assessment; it dramatically improves it while lowering costs and enhancing the candidate
experience. But the longer-term future is anyone’s guess. As the story goes, Henry Ford once
toured a brand-new, semi-automated assembly plant with the head of the United Auto Workers’
Union, Walter Reuther. Feeling pleased with himself, Ford asked Reuther how he planned to
unionize all the new machines. Without missing a beat, Reuther asked Ford how he planned to
sell them cars. Indeed, if AI someday eclipses all human capabilities, who will be left for the robot
recruiters to recruit?
Robots in Recruiting